Object Placement Across Robotics, Image Composition, and XR: A Mixed Reality Framework Proposal

Object placement, a critical task involving the optimal positioning, scaling, and orientation of objects within a given environment, is vital across multiple domains, including robotics, computer vision, and virtual/augmented/mixed reality (VR/AR/MR). Despite its broad applications, no similar study was found that explores object placement across such diverse domains. This paper provides a comprehensive review of existing research on object placement, identifying key stages such as object and environment representation, placement algorithms, and evaluation metrics. A framework for object placement in MR environments is proposed, which integrates geometric and semantic representations, a human-in-the-loop approach, and real-time interaction features. Unlike existing solutions, the framework combines automation with user control, allowing users to adjust placements dynamically while leveraging spatial data for more efficient initialization. The framework was evaluated using both objective and subjective measures. Objective results revealed that the experimental group completed placement tasks 25% faster than the control group, indicating that the framework is effective in reducing task time while maintaining spatial accuracy. Subjective evaluations, indicated a moderate cognitive load, with users reporting significantly lower mental demand and effort and reflected participants’ sense of mastery and control successfully. This study highlights the interdisciplinary nature of object placement research and presents a significant step forward in developing more efficient and adaptable placement solutions. Future studies should explore the integration of multiple technologies and the need for extensive real-world validation.